Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/3435
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dc.contributor.advisorPüskülcü, Halisen
dc.contributor.authorÖzardıç, Onur-
dc.date.accessioned2014-07-22T13:51:31Z-
dc.date.available2014-07-22T13:51:31Z-
dc.date.issued2006en
dc.identifier.urihttp://hdl.handle.net/11147/3435-
dc.descriptionThesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2006en
dc.descriptionIncludes bibliographical references (leaves: 58-64)en
dc.descriptionText in English; Abstract: Turkish and Englishen
dc.descriptionx, 71 leavesen
dc.description.abstractComputer networks are being attacked everyday. Intrusion detection systems are used to detect and reduce effects of these attacks. Signature based intrusion detection systems can only identify known attacks and are ineffective against novel and unknown attacks. Intrusion detection using anomaly detection aims to detect unknown attacks and there exist algorithms developed for this goal. In this study, performance of five anomaly detection algorithms and a signature based intrusion detection system is demonstrated on synthetic and real data sets. A portion of attacks are detected using Snort and SPADE algorithms. PHAD and other algorithms could not detect considerable portion of the attacks in tests due to lack of sufficiently long enough training data.en
dc.language.isoenen_US
dc.publisherIzmir Institute of Technologyen
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.lccTK5105.59 .O991 2006en
dc.subject.lcshComputer networks--Security measuresen
dc.titleStatistical methods used for intrusion detectionen_US
dc.typeMaster Thesisen_US
dc.institutionauthorÖzardıç, Onur-
dc.departmentThesis (Master)--İzmir Institute of Technology, Computer Engineeringen_US
dc.relation.publicationcategoryTezen_US
item.openairetypeMaster Thesis-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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